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Sustainable Engineering Applications of Artificial Intelligence

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 26 July 2024 | Viewed by 1021

Special Issue Editor


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Guest Editor
1. School of Engineering, Aalto University, Espoo, Finland
2. Energy Recovery Inc., San Leandro, CA, USA
Interests: sustainable energy; energy conversion processes; artificial intelligence; engineering applications; fire protection engineering; heat and mass transfer; CFD; physics based modeling; HVAC analysis and design; energy efficiency; energy recovery; combustion; thermal design & control; spectral radiation; remote sensing; spectroscopy; flammability; air quality & control; aerospace engineering
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Special Issue Information

Dear Colleagues,

 In recent years, significant technological advances in artificial intelligence have expanded the capabilities of computers and improved their development capabilities, increasing the sustainability, efficiency, process integration, and intensification of industrial systems. These advancements have opened up broad prospects for technological innovation and automation in various engineering fields. Therefore, the sustainable application of artificial intelligence technology is particularly important, which has also attracted the interest of many scholars. At the same time, there is more and more research on engineering applications based on artificial intelligence technology, and various opportunities and challenges have emerged. This Special Issue will present the most recent advancements in applying artificial intelligence (AI) techniques in sustainable engineering, including supervised and unsupervised learning and classification algorithms, recommenders, reinforcement learning, and deep learning, to various sustainable engineering applications, including but not limited to energy conversion processes, sustainable thermal design, control and management, rechargeable battery engineering, fire protection engineering, remote sensing, autonomous driving, sustainable aerospace engineering, combustion, heat and mass transfer, CFD, wildfire and fire protection engineering. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the application of artificial intelligence, data science, and machine learning in the following areas:·      

  • The design, control, and analysis of engineering systems;
  • Sustainable engineering applications;
  • Spectral radiation heat transfer;
  • Remote sensing and applications;
  • Fire protection engineering;
  • Power generation systems;
  • Enhancement of energy efficiency;
  • Interdisciplinary sustainability research;
  • Environmental technology;
  • HVAC design and analysis;
  • Air quality;
  • Turbomachinery;
  • Sustainable clean energy.

We look forward to receiving your contributions.

Dr. Hadi Bordbar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • sustainable engineering applications
  • energy engineering
  • sustainable energy
  • machine learning
  • classification
  • computer vision
  • fire protection engineering
  • CFD
  • thermal control and design
  • HVAC
  • combustion
  • energy efficiency
  • environmental technology
  • remote sensing
  • spectral radiation
  • air quality
  • automation and control
  • power generation
  • clean energy

Published Papers (1 paper)

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Research

20 pages, 7684 KiB  
Article
CFD—Assisted Expert System for N2-Controlled Atmosphere Process of Rice Storage Silos
by Phakkawat Angsrisuraporn, Chawit Samakkarn, Lertsak Lekawat, Sasathorn Singkhornart and Jatuporn Thongsri
Sustainability 2024, 16(5), 2187; https://doi.org/10.3390/su16052187 - 06 Mar 2024
Viewed by 731
Abstract
Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N2, reducing O2 concentration to [...] Read more.
Since organic rice storage silos were faced with an insect problem, an owner solved this problem using the expert system (ES) in the controlled atmosphere process (CAP) under the required standard, fumigating insects with an N2, reducing O2 concentration to less than 2% for 21 days. This article presents the computational fluid dynamics (CFD) assisted ES successfully solved this problem. First, CFD was employed to determine the gas flow pattern, O2 concentration, proper operating conditions, and a correction factor (K) of silos. As expected, CFD results were consistent with the experimental results and theory, assuring the CFD’s credibility. Significantly, CFD results revealed that the ES controlled N2 distribution throughout the silos and effectively reduced O2 concentration to meet the requirement. Next, the ES was developed based on the inference engine assisted by CFD results and the sweep-through purging principle, and it was implemented in the CAP. Last, the experiments evaluated CAP’s efficacy in controlling O2 concentration and insect extermination in the actual silos. The experimental results and owner’s feedback confirmed the excellent efficacy of ES implementation; therefore, the CAP is effective and practical. The novel aspect of this research is a CFD methodology to create the inference engine and the ES. Full article
(This article belongs to the Special Issue Sustainable Engineering Applications of Artificial Intelligence)
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